Nvidia's Roadmap for GPU Technology Shows Off New Applications

With AMD and Intel recently showing off their roadmaps for new technology, it was Nvidia's turn yesterday, as it discussed it's plans for future graphics processors. But what interested me just as much were a number of other announcements from Nvidia and other companies about using graphics processors (GPUs) in an even larger number of other applications

With AMD and Intel recently showing off their roadmaps for new technology, it was Nvidia's turn yesterday, as it discussed its plans for future graphics processors. But what interested me just as much were a number of other announcements from Nvidia and other companies about using graphics processors (GPUs) in an even larger number of other applications.

At its GPU Technology conference, Nvidia CEO Jen-Hsun Huang disclosed the company's plans for new GPU architectures every two years. The current architecture, code-named Fermi, came out at the end of last year and forms the basis for the company's current midrange and high-end chips, currently built on a 40nm process. This will be followed by an architecture code-named "Kepler," to be built on 28nm technology due out in 2011. In 2013, this is to be followed by "Maxwell" in 2013. (PC Magazine's coverage is here.)

Within this, the performance numbers Huang predicted seem impressive. He said the current Fermi chip can perform an average of 768,000 double-precision floating point operations per second per watt, with a peak of 1.5 gigaflops per watt. He said Kepler set to provide between 3 and 4 times its performance per watt, with Maxwell slated to provide 16 gigaflops per watt or about 16 times the current chip. Some of the improvement obviously comes from process shrinks, but he said other things like pre-emption, virtual memory, and the ability for the GPU to do more processing without waiting for the CPU were important as well. (Of course, note performance depends on applications; and this is for a very specific kind of calculation, not for the typical game.)

More after the jump

Huang talked about how Fermi is being used not only in the GeForce line aimed at gaming, but also in its workstation products and most specifically in Tesla, a line of products aimed at high performance computing. IBM, T-Platforms, and now Cray are all making Tesla servers that replace some traditional CPUs with new Fermi chips.

Of course, we'll have to wait to see final products to really know performance, but in the meantime, I've been intrigued by a couple of other announcements that may have a more immediate impact.

The kind of applications that make sense for CUDA and for GPU Computing in general (which would include the OpenCL standard and Microsoft's DirectCompute) are those that involve lots of parallel activity - specifically "single instruction multiple data" or SIMD applications where you effectively do the same thing to lots of discrete bits of data. Today, perhaps the most obvious consumer application is video encoding or transcoding; but there are lots of high-performance kinds of applications that would also qualify.

Mathworks which makes the Matlab software which is used for engineering, science, and financial modeling, announced support for Nvidia's CUDA software as part of its Parallel Computing Toolbox and Distributed Computing server. In other words, Matlab users can now directly use a Tesla server to speed up parallel applications, without learning CUDA programming. I've been using a third party product, Jacket from Accelereyes, for similar purposes, and found a lot to like. But certainly Mathworks is going to make such computing more mainstream.

Similar enhancements are coming from AMBER, a nano-molecular simulator; and Ansys Mechanical R13, which models how products react to physical changes. Autodesk showed a real-time raytracing demo for rendering photorealistic images in 3D Studio Max.

In addition, the Portland Group (PGI), part of STMicroelectronics, announced that it is developing a CUDA C compiler targeting x86 microprocessors, aimed at the high-performance computing market. What makes this interesting is that it allows for developers to create applications on more standard computers, then move parallel applications to a Tesla-based server for faster production. The company currently offers its PGI Accelerator for C and for Fortran.

Finally, Adobe showed a demo of how they could use a GPU to dramatically speed up assembling an image from component parts, which could be useful in all sorts of effects from correcting blur to creating high-dynamic range images. This isn't a product yet, but points to how a GPU could be used even more in different kinds of applications in the future.

We're also seeing GPU acceleration in a number of consumer applications, from most of the transcoding tools to the upcoming versions of Firefox, Chrome, and Internet Explorer. And with Intel and AMD moving basic GPU functions onto their next generation of consumer processors, it's likely that the combination of traditional CPU and GPU functions together will be a larger part of computing for everyone.

Michael J. Miller's Forward Thinking Blog: forwardthinking.pcmag.com
Michael J. Miller is chief information officer at Ziff Brothers Investments, a private investment firm. From 1991 to 2005, Miller was editor-in-chief of PC Magazine, responsible for the editorial direction, quality and presentation of the world's largest computer publication.
Until late 2006, Miller was the Chief Content Officer for Ziff Davis Media, responsible for overseeing the editorial positions of Ziff Davis's magazines, websites, and events. As Editorial Director for Ziff Davis Publishing since 1997, Miller took an active role in...
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